Why We Need a New Framework for Emotional Intelligence in AI
In this paper, we develop the position that current frameworks for evaluating emotional intelligence (EI) in artificial intelligence (AI) systems need refinement because they do not adequately or comprehensively measure the various aspects of EI relevant in AI. Human EI often involves a phenomenological component and a sense of understanding that artificially intelligent systems lack; therefore, some aspects of EI are irrelevant in evaluating AI systems. However, EI also includes an ability to sense an emotional state, explain it, respond appropriately, and adapt to new contexts (e.g., multicultural), and artificially intelligent systems can do such things to greater or lesser degrees. Several benchmark frameworks specialize in evaluating the capacity of different AI models to perform some tasks related to EI, but these often lack a solid foundation regarding the nature of emotion and what it is to be emotionally intelligent. In this project, we begin by reviewing different theories about emotion and general EI, evaluating the extent to which each is applicable to artificial systems. We then critically evaluate the available benchmark frameworks, identifying where each falls short in light of the account of EI developed in the first section. Lastly, we outline some options for improving evaluation strategies to avoid these shortcomings in EI evaluation in AI systems.
Code (0)
등록된 구현이 없습니다.
Tasks
Emotional IntelligenceSimilar Papers 제목 키워드 기반
EmoLLM: Appraisal-Grounded Cognitive-Emotional Co-Reasoning in Large Language Models
Large language models (LLMs) demonstrate strong cognitive intelligence (IQ), yet many real-world interactions also require emotional intelligence (EQ) to produce responses that are both factually reliable and emotionally…
Reinforcement LearningEmotional IntelligenceEmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models
Emotional intelligence in large language models (LLMs) is of great importance in Natural Language Processing. However, the previous research mainly focus on basic sentiment analysis tasks, such as emotion recognition, wh…
Emotional IntelligenceEmotion RecognitionIntent DetectionSentiment AnalysisEmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Models
With the integration of Multimodal large language models (MLLMs) into robotic systems and various AI applications, embedding emotional intelligence (EI) capabilities into these models is essential for enabling robots to …
BenchmarkingEmotional IntelligenceEmotion RecognitionMME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models
Recent advances in multimodal large language models (MLLMs) have catalyzed transformative progress in affective computing, enabling models to exhibit emergent emotional intelligence. Despite substantial methodological pr…
Emotional IntelligenceEmotion RecognitionEnhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought
Large Language Models (LLMs) have shown remarkable performance in various emotion recognition tasks, thereby piquing the research community's curiosity for exploring their potential in emotional intelligence. However, se…
Emotional IntelligenceEmotion RecognitionSentiment Analysis